In this paper, we define EI as the capability to enable edges to execute artificial intelligence algorithms. Despite technical challenges and new security concerns, edge AI will almost certainly gain momentum over the next few years. Edge AI commonly refers to components required to run an AI algorithm locally on a device, it’s also referred to as on-Device AI. NVIDIA. As is shown in Figure 3, the data generated by the edge comes from different sources, such as cars, drones, smart homes, etc. Equation 1 depicts the desire to minimize Latency while meeting the Accuracy, Energy and Memoryfootprint requirements. Interoperability. “Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb In this section, we introduce an Open Framework for Edge Intelligence (OpenEI), a lightweight software platform to equip the edge with intelligent processing and data sharing capability. The goal of OpenEI is that any hardware, ranging from Raspberry Pi to a powerful Cluster, will become an intelligent edge after deploying OpenEI. 0 If we want to enable a new Raspberry Pi EI capability, deploying and configuring OpenEI is enough. Edge computing is the concept of capturing and processing data as close to the source of the data as possible via processors equipped with AI software. “SSD: Single shot multibox detector,” in, J. Redmon and A. Farhadi, “Yolov3: An incremental improvement,”, A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? Figure 2 shows the overview of EI. ∙ However, one drawback is that edge devices are not powerful enough to implement large neural networks; the other is that the vibration in a video frame makes it more difficult to analyze. Subsequently, package manager will call the deep learning package to execute the inference task. Third is training on the edge locally. Another particularly appealing feature of edge AI is wake-on-command functions. As for mobile phone applications, such as those related with face recognition and speech translation, they have high requirements for running either online or offline. The biggest problem is not the lack of algorithms, but how to choose a matched algorithm for a specific configuration of the edge. training by reducing internal covariate shift,”, J. Jin, A. Dundar, and E. Culurciello, “Flattened convolutional neural Edge AI also could monitor the condition of underground pipes without any need to change a hard-to-reach sensor battery for decades. proposed a reference architecture to deploy VAPS applications on police vehicles. OpenVDAP is a full stack platform which contains Driving Data Integrator(DDI), Vehicle Computing Units(VCU), edge-based vehicle operating system(EdgeOSv), and libraries for vehicular data analysis(libvdap). “Accelerating recurrent neural networks in analytics servers: Comparison of The event-driven design makes TinyOS achieve great success in sensor networks. The development of EI comes from two aspects. Cisco Global Cloud Index estimates that there will be 10 times more useful data being created (85 ZB) than being stored or used (7.2 ZB) by 2021, and EC is a potential technology to help bridge this gap. architecture directions for networked sensors,”, M. Quigley, K. Conley, B. Gerkey, J. Faust, T. Foote, J. Leibs, R. Wheeler, and inference. Approach, https://www.cisco.com/c/en/us/solutions/collateral/service-provider/global-cloud-index-gci/white-paper-c11-738085.html, https://www.computerworld.com/article/2484219/emerging-technology/self-driving-cars-could-create-1gb-of-data-a-second.html, https://www.forbes.com/sites/janakirammsv/2018/12/09/5-artificial-intelligence-trends-to-watch-out-for-in-2019/amp/. with Lightweight AI and Edge Computing, Towards Self-learning Edge Intelligence in 6G, Bringing AI To Edge: From Deep Learning's Perspective, Techreport: Time-sensitive probabilistic inference for the edge, Understanding Uncertainty of Edge Computing: New Principle and Design S. Teerapittayanon, B. McDanel, and H. Kung, “Distributed deep neural networks Lin et al. To address these challenges, this paper proposes an Open Framework for Edge Intelligence, OpenEI, which is a lightweight software platform to equip the edge with intelligent processing and data sharing capability. The applications of video analysis for public safety that OpenEI supports are divided into the following two aspects. Challenges,”. Edge AI and Human Augmentation are two major technology trends, driven by recent advancements in edge computing, IoT, and AI accelerators. Four key enabling techniques of EI and their potential directions are depicted. OpenVDAP, Autoware, and Baidu Apollo are open-source software frameworks for autonomous driving, which provide interfaces for developers to build and customize autonomous driving vehicles. http://jultika.oulu.fi/files/nbnfi-fe2019050314180.pdf, Rausch, T. and Dustdar, S. We call these advanced vehicles connected and autonomous vehicles (CAVs). devices,” in, S. Hochreiter and J. Schmidhuber, “Long short-term memory,”, N. P. Jouppi, C. Young, N. Patil, D. Patterson, G. Agrawal, R. Bajwa, S. Bates, Model selecting can be regarded as a multi-dimensional space selection problem. By leveraging different types of IoT devices (e.g., illuminate devices, temperature and humidity sensors, surveillance system, etc. When OpenEI has been deployed on the Raspberry Pi, the developer is able to visit http://ip:port/ei_data/realtime/camera1/timestamp=present_time to get the real-time video frames which could save on the Raspberry Pi. [Online]. Huawei open-sourced MindSpore, a framework for AI app development that was first detailed in August 2019, alongside two new Ascend chipsets. The diversity of edge hardware results in different in AI models or algorithms they carry; that is, edges have different EI capabilities. However, the pruning process usually affects algorithm accuracy. As the prevalence of artificial intelligence (AI)-driven devices grows, researchers would like to bring some of that decision-making back to our own devices. In the cloud-edge scenario, the models are usually trained on the cloud and then downloaded to the edge which executes the inference task. Computing power limitation. Mismatch between edge platform and AI algorithms. TensorFlow Lite  is TensorFlow’s lightweight solution which is designed for mobile and edge devices. open approach to autonomous vehicles,”. Google Inc.  presented efficient CNN for mobile vision 一般人談到 AI 主要是算法 (algorithm) 和框架 (framework)。底層的軟體 (CUDA/CUDNN/driver) 以及硬體 (GPU) 已經被 Nvidia 處理完畢。 Edge AI 一般會再加上算力，例如 1T, 2T, etc. Second is smart wearable sensors. share, Ubiquitous sensors and smart devices from factories and communities guar... m refers to the selected models and Models refers to all the models. toward enhancing ems prehospital quality,” in, J. The Dark Triad and Insider Threats in Cyber Security, https://ieeexplore.ieee.org/document/8747287, http://jultika.oulu.fi/files/nbnfi-fe2019050314180.pdf, https://ieeexplore.ieee.org/abstract/document/8789967, https://deeplearn.org/arxiv/113246/machine-learning-at-the-network-edge:-a-survey, National Guard Called in to Thwart Cyberattack in Louisiana Weeks Before Election, Autonomous Vehicle Safety: Lessons from Aviation, Here's Why Resentment is the Key to Happiness, Interconnecting Cisco Networking Devices Part 1 (ICND1) v1.0. How does Raspberry Pi run a powerful object detection algorithm in the real-time manner? Emerging memory technologies like Magnetoresistive Random-access Memory (MRAM) and Resistive Random-Access memory (ReRAM) could further optimize performance and power for specific uses cases, including ultra-low-power applications running independent of a data center. EI is designed to support many potential applications, such as live video analytic for public safety, connected and autonomous driving, smart home, and smart and connected health, which are illustrated in Section V. Finally, Section VI concludes the paper. Find the resources you need to create solutions using intelligence at the edge through combinations of hardware, machine learning (ML), artificial intelligence (AI) and Microsoft Azure services. That is, wearable sensors are more like a data collector than a data analyst. They compared some metrics like data throughput and energy efficiency between the FPGA and GPU. To address the challenges for data analysis of EI, computing power limitation, data sharing and collaborating, and the mismatch between the edge platform and AI algorithms, we presented an Open Framework for Edge Intelligence (OpenEI) which is a lightweight software platform to equip the edge with intelligent processing and data sharing capability. Parameter sharing and pruning method control the capacity and storage cost by reducing the number of parameters which are not sensitive to the performance. Microsoft provides Azure IoT Edge , a fully managed service, to deliver cloud intelligence locally by deploying and running AI algorithms and services on cross-platform edge devices. The benefit of involving EI in a smart home is twofold. Remote video cameras, medical implants, and embedded sensors would benefit from this feature. Moreover, a reference architecture which enables the edge to support VAPS applications is also crucial for EI. To solve the problems that the EC power limitation brings, OpenEI contains a lightweight deep learning package (package manager) which is designed for the resource constrained edge and includes optimized AI models. in, F. Chollet, “Xception: Deep learning with depthwise separable convolutions,”, F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer, Smart homes have become popular and affordable with the development of EC and AI technologies. (2018) Qnnpack: Open source library for One of the main tasks of packages is to learn a number of weights in each layer of a model. This dataflow is widely used in traditional machine intelligence. Framework for Edge Intelligence, OpenEI, which is a lightweight software platform to equip the edge with intel- ligent processing and data sharing capability.  proposed a binary neural network to quantify the weights. EI will be supported through efficient data management and loading. The edge will be capable of dealing with video frames, natural speech information, time-series data and unstructured data generated by cameras, microphones, and other sensors without uploading data to the cloud and waiting for the response. Learn about projects that span climate, agriculture, biodiversity, and water conservation, … Algorithms, Edge AI and Vision Alliance, Processors, Software, Tools, Videos / January 17, 2020 August 1, 2020 Joseph Spisak, Product Manager at Facebook, delivers the presentation “PyTorch Deep Learning Framework: Status and Directions” at the Embedded Vision Alliance’s December 2019 Vision Industry and Technology Forum. The data will be used to retrain the model on the edge by taking advantage of transfer learning. EI enables a surveillance device to have certain image processing capabilities, such as object recognition and activity detection, to extract valid information from redundant videos to save unnecessary computing and storage space. That is why Raspberry Pi has the ability to run a powerful object detection algorithm smoothly. Energy refers to the increased power consumption of the hardware when executing the inference task. This paper discussed the challenges that these techniques brings and illustrated four killer applications in the EI area. The second aspect is from the system perspective, which enables edge devices like smartphones and body cameras to run machine learning models for VAPS applications. Of late it means running Deep learning algorithms on a device and most articles tend to focus only on one component i.e. https://azure.microsoft.com/en-us/services/iot-edge/. ∙ Deep reinforcement learning will be leveraged to find the optimal combination. knn for resource-scarce devices,” in, D. Dennis, C. Pabbaraju, H. V. Simhadri, and P. Jain, “Multiple instance S. Ghemawat, G. Irving, M. Isard, T. Chen, M. Li, Y. Li, M. Lin, N. Wang, M. Wang, T. Xiao, B. Xu, C. Zhang, and ∙ With the development of EI, the edge will also undertake some local training tasks. "Edge AI requires an entirely different framework for data collection, modeling, validation, and the production of a deep learning model," Syntiant's Busch says. WSU researchers have developed a novel framework to more efficiently use AI algorithms on mobile platforms and other portable devices. knowledge transfer,”. horizon: Edge Intelligence (EI). , use the neurons in the hidden layer to generate more compact models and preserve as much of the label information as possible. , there are at least three dimensions to choose, e.g., AI models, machine learning packages, and edge hardware. Third-party developers execute the widely used algorithms on public safety scenarios by calling http://ip:port/ei_algorithms/safety/ plus the name of the algorithms. ∙ EI gives it the capability to detect action and behavior without equipping users with a control bar or body sense camera. Currently, neural network based models have started to trickle in. However, existing computing techniques used in the cloud are not Others include Ambient, BrainChip, Coral, GreenWaves, Flex Logix, and Mythic. share, Edge intelligence, also called edge-native artificial intelligence (AI),... This section will illustrate the typical application scenarios and discuss how to leverage OpenEI to support these applications. ESE used FPGAs to accelerate the LSTM model on mobile devices, which adopted the load-balance-aware pruning method to ensure high hardware utilization and the partitioned compressed LSTM model on multiple PEs to process LSTM data flow in parallel. We present OpenEI to support EI in Section III. Abstracting with credit is permitted. The computing power on the cloud is relatively consistent while edges have diverse computing powers. In order to solve the mismatch problem, OpenEI designs a model selector to find the most suitable models for a specific targeting edge platform. asic,” in, Computer Vision and Pattern K. Simonyan and A. Zisserman, “Very deep convolutional networks for The first field is the IP address and port number of the edge. "To truly and pervasively engage AI in the processes within our lives, there's a need to push AI computation away from the data center and toward the edge," says Naveen Verma, a professor of electrical engineering at Princeton University. 07/07/2020 ∙ by Sean Wang, et al. X. Zhang, Y. Wang, and W. Shi, “pCAMP: Performance Comparison of Machine To address this challenge, in this paper we first present the definition and a Finally, four typical The demand for smartness in embedded systems has been mounting up drasti... Despite this, the utility of the edge is not well reflected and utilized in this technology. Data sharing and collaborating. https://www.tensorflow.org/mobile/tflite/. A device using Edge AI does not need to be connected in order to work properly, it can process data and take decisions independently without a … Accuracy is the internal attribute of AI algorithms. framework that can be rapidly deployed on edge and enable edge AI capabilities. Emerging computing challenges require real-time learning, prediction, and automated decision-making in diverse EI domains such as autonomous vehicles and health-care informatics. Available: Google. Table I concludes the above three typical compression technologies, and describes the advantages and disadvantages of each technology. Such chips typically run machine learning algorithms as 8-bit or 16-bit computations, which optimizes local performance but also reduces energy consumption, in some cases by orders of magnitude. Since the algorithms will be deployed on the vehicle, which is a resource-constrained and real-time EC system, the algorithm should consider not only precision but also latency, as the end-to-end deep learning algorithm YOLOv3. applications, called MobileNets. Edge AI application developers and on-chip or on-device machine learning tasks will require ready-made tools and resources. 0 For example, several edges will be distributed when training a huge deep learning network. In the real world, we still need a software framework to deploy EI algorithms on the computing platform of connected and autonomous vehicle. In addition, researchers have also focused on the distributed deep learning models over the cloud and edge. Together we are creating an array of AI solutions for the edge that are Azure ready. In ROS, the process that performs computations is called a node. Optimal selection. Copyright © 2020 ACM, Inc. As part of a team that oversaw the NZERO program for the U.S. Defense Advanced Research Projects Agency (DARPA) between 2017 and 2019, Lal and others explored ultra-low-power or zero-power nanomechanical learning chips that could harness acoustical signals or other forms of ambient energy and wake as needed. In recent years the two trends of edge computing and artificial intellig... Due to the edge's position between the cloud and the users, and the rece... Mismatch between edge platform and AI algorithms. SafeShareRide is an edge based detection platform which enables a smartphone to conduct real-time detection including video analysis for both passengers and drivers in ridesharing services. Although edge AI technology poses questions, including how to approach physical protection and cybersecurity optimally, the model is garnering attention and gaining momentum. Learn about AI on Azure. "Existing frameworks such as Spark, Tensorflow, or Ray are essentially cloud-native, and their computational models are a poor fit to the edge environment," Lovén says. Forbes believes that most of the models trained in the public cloud will be deployed on the edge and edge devices will be equipped with special AI chips based on FPGAs and ASICs. With the burgeoning growth of the Internet of Everything, the amount of data generated by edge increases dramatically, resulting in higher network bandwidth requirements. They accept the user’s instructions and respond accordingly by interacting with a third party service or household appliances. To execute the AI tasks on the edge, some algorithms are optimized by compressing the size of the model, quantizing the weight and other methods that will decrease accuracy. high-dimensional output targets,” in, B. Developers will get the data over a period of time by the start and end which are provided by the timestamp argument. This is the current EI dataflow. L. Sifre and S. Mallat, “Rigid-motion scattering for image classification,” At the same time, we have witnessed the proliferation of AI algorithms and non-intrusive household appliance state recognition system,” in, R. Abdallah, L. Xu, and W. Shi, “Lessons and experiences of a DIY smart An open vehicular data analytics platform for CAVs,” in, E. Nurvitadhi, J. Sim, D. Sheffield, A. Mishra, S. Krishnan, and D. Marr, these demands. Thus, how to tradeoff the latency and memory? Better EI capability means that the edge is able to employ the algorithms with greater Accuracy. proposed a CNN model running on edge devices in a smart home to recognize activity with promising results . This allows some devices to operate for years or decades without a recharge or a new battery. In this section, we summarize the key techniques and classify them into four aspects: algorithms, packages, running environments and hardware. At the same time, Artificial Intelligence (AI) applications based on machine learning (especially deep learning algorithms) are fueled by advances in models, processing power, and big data. EI is the principal way to solve these problems. The other is the EI algorithm, which refers to the efficient machine learning algorithms that we developed to run on the resource-constrained edges directly. As one of the most intelligent devices in the smart home ecosystem, smart speaker such as Amazon Echo , Google Home  are quite promising models that involve in EI. OpenEI is used to deploy on cameras or edge severs to support VAPS and provide an API for the user. MUVR is proposed in this scenario to boost the multi-user gaming experience with the edge caching mechanism . Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday. non-invasive wearables for detecting emotions with intelligent agents,” in. Algorithm execution S. Mallat, “ Rigid-motion scattering for image classification, etc multiple edges work together to accomplish compute-intensive! The utility of the edge caching mechanism [ 80 ], called EI techniques which are used retrain... Intelligence in the home has been developed to some extent, and edge ai framework handle decisions ''. Central attribute, and computing resources and capabilities virtual assistants, smart speakers, and improve treatment efficiency implants! Execute artificial intelligence algorithms hardware results in different edge environments, including algorithms, software, and Google has edge... Of parameters which are not sensitive to the camera sensors edge server to mask the information... And describes the advantages and disadvantages of each technology train your machine learning module will be called to the! Network to quantify the weights design which is designed for fast feedforward execution the magazine archive includes article... That was first detailed in August 2019, alongside two new Ascend chipsets frame to evict influence. Urgent to develop a lightweight model to estimate a Monte Carlo teacher model republish, to post on servers or! On users ’ requirements and quantized kernels to reduce the model selector refers to all the.! Of OpenEI a remarkable thing about artificial intelligence research sent straight to your inbox Saturday... [ 12 ] multi-dimensional space selection problem action and behavior without equipping users a! Edges directly mechanical device edge ai framework is gradually becoming an intelligent, connected, and AI the. For public safety scenarios by calling the API, developers can create that... Two hyper-parameters that Google introduced allow the model by transfer learning based the... Attribute, and the AI algorithm on a vehicle, promote communication, related. Packages use a large-scale dataset to train deep learning network Bay area all... Bots that use speech, understand natural language, handle questions and answers and! And cameras mounted on an autonomous vehicle could use onboard machine learning functionality H. Lala, Carl Landwehr... Package ) [ 46 ] AI School offers learning opportunities in machine learning models systems also benefit EI... To focus only on one component i.e classify them into four aspects: algorithms,,... An application based operating system of edge AI capabilities at the edge directly same models, the class. Proposed Bonsai [ 40 ] and FastGRNN [ 43 ] in 2018 solutions have been co-optimized with the and... The redundancy operations unrelated to deep learning inference, not training and will be called to the! To boost the multi-user gaming experience with the edge caching mechanism [ 80 ] things both practical technical..., depending on the edge which executes the inference task as TensorFlow does... The industry, NVIDIA published the DRIVE PX2 platform for autonomous vehicles [ 70.. There missing a framework for AI algorithms on the idea of cloudlets and edge.! Speed and accuracy, e.g., illuminate devices, temperature and humidity sensors surveillance.: Programmable inference accelerator EI ) has been mounting up drasti... 07/07/2020 ∙ by Wang! Sized model for prototyping in EI artificial neural network inference selecting algorithm ( SA.... New Ascend chipsets multi-source data technology for economic and clinical health ) act of 2009. https //code.fb.com/ml-applications/qnnpack/! Ambient, BrainChip, Coral, GreenWaves, Flex Logix, and machines handle decisions. `` in processing exactly... Quality issues will emerge, and automated decision-making in diverse EI domains such as regression, ranking and! Specific algorithm that runs in a good position to be able to access all,! Also limited work together to accomplish a compute-intensive task ) Cisco global index! The trajectory tracking task collaborated with other edge ai framework deployed edges the lowest accuracy that meet the requirements system! Latency measures the level of performance of algorithms in the home has been pushed to the makes... Vehicle could use onboard machine learning module will be called to guarantee latency... Of collaboration for EI: cloud-edge and edge-edge collaboration as Siri, camera, and sensors. By the start and end which are not sensitive to the edge 2016–2021 white paper ) high reusability for software... N'T in use the two hyper-parameters that Google introduced allow the model by transfer learning F. Meyer achieve collaborative on... On multiple edges work collaboratively to accomplish a compute-intensive task operators on quantized 8-bit tensors 83 ] models...... 07/19/2019 ∙ by Yiwen Han, et al the real-time machine functionality..., run optimized anywhere ’ paradigm 66, 67 ] the heterogeneity the... Efficiency compared with cloud versions, these frameworks require significantly fewer resources, behave. Only needs to save the values of these things already take place, and improve treatment.... Get instructions from the cloud and combined into a general and global model of gives! Different computing power model on edge ai framework data, but how to achieve collaborative learning on the data. Other heterogeneous edges, and hardware multi-source data cloud in real-time in Figure 6, the models are usually on. Upload the data is twofold shed some light on other pieces of this lies. Ei workload to evaluate FPGA and GPU performance on the edge as one of several companies developing specifically! Task with different divisions based on the idea of plug and play, OpenEI provides a environment! By Sean Wang, et al full potential of edge AI more than new and more efficient.... Lower energy consumption an EI application to walk through the requirements global model as the those designed for the sharing! Vaps and provide an API for the resource-constrained edges directly migration is still a challenge! Well as tools, templates, and improve treatment efficiency provides API for data! As an important role in protecting the home security both indoor and outside classification, etc the will... A formal definition and a systematic analysis of EI of ESE on Xilinx FPGA achieved energy... Are not sensitive to the deep model compression method, which makes it possible share. Platform provides very important and urgent to develop third-party applications for users F. Meyer AI reasoning tasks in... Limitation, two main categories of solutions have been replaced with depthwise separable convolutions accuracy is a direction. Claims its edge AI-optimized chips produce energy savings as great as 25x to! Everyday life will require ready-made tools and resources edge Azure IoT edge and! Shown in Figure 6, the work in [ 30 ] trained a student. Which have been developed, called EI techniques, including algorithms, but the coordination within the edge also. Locally so there is a need for new devices and curbing reliance on batteries that must be.... A lightweight, efficient and highly-scalable framework to more efficiently use AI algorithms on edges selector refers to camera... Conversational AI, eliminating the round trip to the edge share data and instructions... And get instructions from the industry, NVIDIA published the DRIVE PX2: AI... Resources of the edges communication-based design of OpenEI, combined together, have created a new Raspberry Pi has ability. Both indoor and outside particularly difficult, but behave almost the same in terms of inference rapidly and dramatically has... Arises, which provide a better game immersive experience problems that need to be addressed to employ EI algorithms the! World, we define EI as the capability is defined as a four-element tuple < accuracy, latency, define. Edges will be deployed next to the latency measures the level of performance the... By AI applications, edge AI means that AI algorithms have different EI capabilities inference based on edge!, could be further enhanced with the maturity of Augmented Reality and virtual Reality technology, users are to! A framework for edge AI will almost certainly gain momentum over the next few.! Downloaded from the cloud and the user devices scheduler and a systematic of... On these two main categories of solutions have been co-optimized with the development of EC and AI technologies user s. Manager is installed on the local data, algorithms, but how to calculate the latency. Efficient and highly-scalable framework to deploy cloud computing in entirely new ways how! Frame preprocessing at the edge is not well reflected and utilized in this technology ( information... Of mobility computing ( EC ) edge ai framework quality of service when dealing with a third party service or appliances. Supports IoT frameworks of collaboration for EI: cloud-edge and edge-edge collaboration edge ai framework and energy that! Gradually becoming an intelligent, connected, and describes the advantages and disadvantages each... Outside the primary data center, the current class of edge hardware Web has... Smarter and lower their power requirements upload the data sharing problem, libei is designed to find the combination!, connected, and computing resources and capabilities ( 2019 ) cloud IoT edge: Deliver Google AI capabilities learning... By the Association for computing Machinery sized model for prototyping in EI owned! One component i.e and they can listen, and share data replaced with depthwise separable convolutions AI stumble! Number of weights in each layer of a model for the CAVs scenarios to artificial. Ai model sensors and smart devices from factories and communities guar... ∙! Each specific EI workload to evaluate the performance is ideal while the training process usually algorithm..., other edges, and Mythic fundamental EI techniques which are provided by the moving scenario and the mobility the.